Abstract
Marine ecosystem models such as Atlantis have valuable insight into multispecies interactions, spatial dynamics and fisheries management, but their high computational cost limits are rapid scenario analysis and real-time decision making. This study presented a data-driven ecosystem emulator for the Northeast U.S. Atlantis model using automated machine learning approaches. A large-scale dataset (~3.4 million records) spanning 1964-2020 was contracted, integrating biomass of species functional groups, spatial polygons temporal indices and environments variables such as temperature and salinity. The emulator framework employed automated machine learning techniques, including Random Forest and Extra tress regression, with model selection and hyperparameter optimization performance using Automatic Machine Learning strategies. In addition, Autokeras was utilized to explore neural network architecture in an automated manner, enabling data-driven model allocation, lagged variables to capture ecological inertia and time-aware transformations. Model performance was evaluated using out-of-sample temporal validation, recursive back testing and ecological plausibility assessments. Result demonstrated string predictive performance. with species-level R2 values frequently exceeding 0.90 and overall model accuracy approaching 94%. The emulator achieved high computational efficiency, with end-to-end prediction completed in under few seconds, substantially reducing runtime compared to Atlantis simulations. This work established a scalable and efficient AutoML-driven alternative to process -based ecosystem, model, enabling rapid biomass estimation and supporting data-driven fisheries management and ecosystem analysis.